Background Improving care quality while reducing cost has always been a focus of nursing homes. Certified nursing assistants comprise the largest proportion of the workforce in nursing homes and have the potential to contribute to the quality of care provided. Quality improvement initiatives using certified nursing assistants as champions have the potential to improve job satisfaction, which has been associated with care quality. Aims To identify the role, use and preparation of champions in a nursing home setting as a way of informing future quality improvement strategies in nursing homes. Methods A systematic literature review. Medical Subject Headings and text words for “quality improvement” were combined with those for “champion*” to search Medline, CINAHL, Joanna Briggs Institute, MedLine In-Process and other Nonindexed Citations. After duplicates were removed a total of 337 potential articles were identified for further review. After full text review, seven articles from five original studies met inclusion criteria and were included in the synthesis. Results Various types of quality improvement initiatives and implementation strategies were used together with champions. Champions were identified by study authors as one of the single most effective strategies employed in all studies. The majority of studies described the champion role as that of a leader, who fosters and reinforces changes for improvement. Although all the included studies suggested that implementing nurse or aid champions in their quality improvement initiatives were important facilitators of success, how the champions were selected and trained in their role is either missing or not described in any detail in the studies included in the review. Linking Evidence to Action Utilizing certified nursing assistants as quality improvement champions can increase participation in quality improvement projects and has the potential to improve job satisfaction and contribute to improve quality of care and improved patient outcomes in nursing homes.

Despite evidence of effectiveness for telehealth, there is a high rate of telehealth refusal among patients. Understanding factors associated with heart failure patients' decisions regarding telehealth can help healthcare organizations structure education programs and other interventions to improve acceptance rates.

Background: About 30% of home health care patients are hospitalized or visit an emergency department (ED) during a home health care (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes. Objectives:To identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes).Methods: This study used a large database of HHC visit notes (n = 727,676) documented for 112,237 HHC episodes (89,459 unique patients) by clinicians of the largest nonprofit home health care agency in the United States. Text mining and machine learning algorithms (Naïve Bayes, decision tree, random forest) were implemented to predict patient hospitalization or ED visits using the content of clinical notes. Risk factors associated with hospitalization or ED visits were identified using a feature selection technique (gain ratio attribute evaluation). Results:Best performing text mining method (random forest) achieved good predictive performance. Seven risk factors categories were identified, with clinical factors, coordination/ communication, and service use being the most frequent categories.Discussion: This study was the first to explore the potential contribution of HHC clinical notes to identifying patients at risk for hospitalization or an ED visit. Our results suggest that HHC visit notes are highly informative and can contribute significantly to identification of patients at risk. Further studies are needed to explore ways to improve risk prediction by adding more data elements from additional data sources. Keywordshome health care; natural language processing; nursing informatics; risk prediction; text mining Every year, more than 11,000 home health care (HHC) agencies across the United States provide care to more than 5 million older adults (MedPac, 2014). Currently, about one in three HHC patients are hospitalized or visit an emergency department (ED) during the 30-

Background: Little is known about symptom documentation related to Alzheimer’s disease and related dementias (ADRD) by home healthcare (HHC) clinicians. Objective: This study: (1) developed a natural language processing (NLP) algorithm that identifies common neuropsychiatric symptoms of ADRD in HHC free-text clinical notes; (2) described symptom clusters and hospitalization or emergency department (ED) visit rates for patients with and without these symptoms. Method: We examined a corpus of −2.6 million free-text notes for 112,237 HHC episodes among 89,459 patients admitted to a non-profit HHC agency for post-acute care with any diagnosis. We used NLP software (NimbleMiner) to construct indicators of six neuropsychiatric symptoms. Structured HHC assessment data were used to identify known ADRD diagnoses and construct measures of hospitalization/ED use during HHC. Results: Neuropsychiatric symptoms were documented for 40% of episodes. Common clusters included impaired memory, anxiety and/or depressed mood. One in three episodes without an ADRD diagnosis had documented symptoms. Hospitalization/ED rates increased with one or more symptoms present. Conclusion: HHC providers should examine episodes with neuropsychiatric symptoms but no ADRD diagnoses to determine whether ADRD diagnosis was missed or to recommend ADRD evaluation. NLP-generated symptom indicators can help to identify high-risk patients for targeted interventions.

Telehealth has been reported to be effective in helping patients with heart failure manage their symptoms at home. Despite this, the adoption rate for telehealth among home care patients with heart failure is low, and there is limited research on reasons for this. This study was undertaken to explore factors associated with patients' decisions to adopt telehealth at home. A qualitative descriptive study underpinned by the Unified Theory of Acceptance Use of Technology model was conducted using semi-structured telephone interviews with patients with heart failure (N = 20) referred for telehealth. Interviews were analyzed using a mixture of deductive and inductive coding. Among the theoretical model elements, the perceived usefulness of the technology (performance expectancy), the availability of clinical/technical support (facilitating conditions), and the opinion of other individuals important to the patient (social influence) were associated with telehealth initiation. However, the ease of use (effort expectancy) was not an associated factor. Other factors such as experience, knowledge, confidence, satisfaction, and attitudes were also associated with the decision. Identification of factors related to higher telehealth initiation rates can be used to inform individualized care planning by nurses. Knowledge of such associations can inform referral process to improve the efficiency and utilization of telehealth.

Objectives: Urinary tract infection (UTI) is common in home care but not easily captured with standard assessment. This study aimed to examine the value of nursing notes in detecting UTI signs and symptoms in home care. Design: The study developed a natural language processing (NLP) algorithm to automatically identify UTI-related information in nursing notes. Setting and Participants: Home care visit notes (n = 1,149,586) and care coordination notes (n = 1,461,171) for 89,459 patients treated in the largest nonprofit home care agency in the United States during 2014. Measures: We generated 6 categories of UTI-related information from literature and used the Unified Medical Language System (UMLS) to identify a preliminary list of terms. The NLP algorithm was tested on a gold standard set of 300 clinical notes annotated by clinical experts. We used structured Outcome and Assessment Information Set data to extract the frequency of UTI-related emergency department (ED) visits or hospitalizations and explored time-patterns in documentation of UTI-related information. Results: The NLP system achieved very good overall performance (F measure = 0.9, 95% CI: 0.87–0.93) based on the test results obtained by using the notes for patients admitted to the ED or hospital due to UTI. UTI-related information was significantly more prevalent ( P < .01 for all the tests) in home care episodes with UTI-related ED admission or hospitalization vs the general patient population; 81% of home care episodes with UTI-related hospitalization or ED admission had at least 1 category of UTI-related information vs 21.6% among episodes without UTI-related hospitalization or ED admission. Frequency of UTI-related information documentation increased in advance of UTI-related hospitalization or ED admission, peaking within a few days before the event. Conclusions and Implications: Information in nursing notes is often overlooked by stakeholders and not integrated into predictive modeling for decision-making support, but our findings highlight their value in early risk identification and care guidance. Health care administrators should consider using NLP to extract clinical data from nursing notes to improve early detection and treatment, which may lead to quality improvement and cost reduction.

We aimed to create and validate a natural language processing algorithm to extract wound infection‐related information from nursing notes. We also estimated wound infection prevalence in homecare settings and described related patient characteristics. In this retrospective cohort study, a natural language processing algorithm was developed and validated against a gold standard testing set. Cases with wound infection were identified using the algorithm and linked to Outcome and Assessment Information Set data to identify related patient characteristics. The final version of the natural language processing vocabulary contained 3914 terms and expressions related to the presence of wound infection. The natural language processing algorithm achieved overall good performance (F‐measure = 0.88). The presence of wound infection was documented for 1.03% (n = 602) of patients without wounds, for 5.95% (n = 3232) of patients with wounds, and 19.19% (n = 152) of patients with wound‐related hospitalisation or emergency department visits. Diabetes, peripheral vascular disease, and skin ulcer were significantly associated with wound infection among homecare patients. Our findings suggest that nurses frequently document wound infection‐related information. The use of natural language processing demonstrated that valuable information can be extracted from nursing notes which can be used to improve our understanding of the care needs of people receiving homecare. By linking findings from clinical nursing notes with additional structured data, we can analyse related patients' characteristics and use them to develop a tailored intervention that may potentially lead to reduced wound infection‐related hospitalizations.

OBJECTIVE Wound infection is prevalent in home healthcare (HHC) and often leads to hospitalizations. However, none of the previous studies of wounds in HHC have used data from clinical notes. Therefore, the authors created a more accurate description of a patient’s condition by extracting risk factors from clinical notes to build predictive models to identify a patient’s risk of wound infection in HHC. METHODS The structured data (eg, standardized assessments) and unstructured information (eg, narrative-free text charting) were retrospectively reviewed for HHC patients with wounds who were served by a large HHC agency in 2014. Wound infection risk factors were identified through bivariate analysis and stepwise variable selection. Risk predictive performance of three machine learning models (logistic regression, random forest, and artificial neural network) was compared. RESULTS A total of 754 of 54,316 patients (1.39%) had a hospitalization or ED visit related to wound infection. In the bivariate logistic regression, language describing wound type in the patient’s clinical notes was strongly associated with risk (odds ratio, 9.94; P < .05). The areas under the curve were 0.82 in logistic regression, 0.75 in random forest, and 0.78 in artificial neural network. Risk prediction performance of the models improved (by up to 13.2%) after adding risk factors extracted from clinical notes. CONCLUSIONS Logistic regression showed the best risk prediction performance in prediction of wound infection-related hospitalization or ED visits in HHC. The use of data extracted from clinical notes can improve the performance of risk prediction models.